Natural Hazards

, Volume 63, Issue 3, pp 1375–1391 | Cite as

On the initialization of tropical cyclones with a three dimensional variational analysis

  • Chi-Sann Liou
  • Keith D. Sashegyi
Original Paper


A method of initializing tropical cyclones in high-resolution numerical models is developed by modifying a data assimilation system, the NRL atmospheric variational data assimilation system (NAVDAS), which was designed for general mesoscale weather prediction using a three-dimensional variational (3DVAR) analysis with intermittent updates. The method includes the following three upgrades to overcome difficulties resulting from tropical cyclone initialization with the NAVDAS analysis. First, synthetic observation soundings are generated on 9 vertical levels at 49 points for strong storms (v max > 23.1 m s−1) and 41 points for weak storms around each cyclone center to supplement the observations used by the analysis. Secondly, a vortex relocation method for nested grids is developed to correct the cyclone position in the background fields of the analysis for each nested mesh. Lastly, the 3DVAR analysis is modified to gradually reduce the horizontal length scale and geostrophic coupling constraint near the center of a tropical cyclone for minimizing the problems introduced by improper covariances and coupling constraint used in the analysis. The synthetic observations significantly improve the intensity and structure of the analysis and the track forecast. The vortex relocation significantly improves the first guess background, avoiding the large analysis corrections that would be needed to correct cyclone position, and reducing the imbalance introduced by such large analysis increments. The modifications to the analysis length scale and geostrophic coupling constraint successfully improve the inner core analysis, providing a tighter circulation, and reducing the underestimate of the mass field gradient. Among the three upgrades, the vortex relocation provides the largest improvement to the tropical cyclone initialization and forecast.


Tropical cyclone initialization Synthetic observations Vortex relocation 3DVAR analysis for tropical cyclones 



This research is supported by the Office of Naval Research through program PE-0602435 N. We benefit from the discussions with the rest of COAMPS-TC development team members, especially Drs. James Doyle and Richard Hodur. We also appreciate comments and suggestions from two anonymous reviewers.


  1. Barnes SL (1994) Application of the Barnes objective analysis scheme. Part I: effects of under sampling, wave position and station randomness. J Atmos Oceanic Tech 11:1433–1448CrossRefGoogle Scholar
  2. Collins WG, Gandin LS (1990) Comprehensive hydrostatic quality-control at the national meteorological center. Mon Wea Rev 118:2752–2767CrossRefGoogle Scholar
  3. Daley R (1991) Atmospheric data analysis. Cambridge University Press, Cambridge, p 457Google Scholar
  4. Daley R, Barker E (2000) NAVDAS—formulation and diagnostics. Mon Wea Rev 129:869–883CrossRefGoogle Scholar
  5. Daley R, Barker E (2001) The NAVDAS source book 2001. NRL/PU/7530–01-441. Naval Research Laboratory, Monterey, p 163Google Scholar
  6. Evensen G (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99(C5):10143–10162CrossRefGoogle Scholar
  7. Gandin LS, Morone LL, Collins WG (1993) Two years of operational comprehensive hydrostatic quality-control at the national meteorological center. Wea Forecast 8:57–72CrossRefGoogle Scholar
  8. Hamill TM, Snyder C (2000) A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon Wea Rev 128:2905–2912CrossRefGoogle Scholar
  9. Hsiao L-F, Liou C-S, Yeh T-C, Guo Y-R, Chen D-S, Huang K-N, Terng C-T, Chen J-H (2010) A vortex relocation scheme for tropical cyclone initialization in advanced research WRF. Mon Wea Rev 138:3298–3315CrossRefGoogle Scholar
  10. Kurihara Y, Bender MA, Tuleya RE, Ross RJ (1995) Improvements in the GFDL hurricane prediction system. Mon Wea Rev 123:2791–2801CrossRefGoogle Scholar
  11. Liu Q, Marchok T, Pan H, Bender M, Lord S (2000) Improvements in hurricane initialization and forecasting at NCEP with global and regional (GFDL) models. NCEP/EMC Tech Proc Bull 472:7Google Scholar
  12. Liu Q, Surgi N, Lord S, Wu W-S, Parrish D, Gopalakrishnan S, Waldrop J. Gamache J (2006) Hurricane initialization in HWRF model. 27th Conference on hurricanes and tropical meteorology, Monterey, CA, Amer. Meteor. Soc., P5.13Google Scholar
  13. Pauley PM (2003) Superobbing satellite winds for NAVDAS. NRL/MR/7530–03-8670. Naval Research Laboratory, Monterey, p 102Google Scholar
  14. Wang X (2010) Incorporating ensemble covariance in the grid point statistical interpolation (GSI) variational minimization: a mathematical framework. Mon Wea Rev 138:2990–2995CrossRefGoogle Scholar

Copyright information

© US Government 2011

Authors and Affiliations

  1. 1.Naval Research Laboratory, Marine Meteorology DivisionMontereyUSA

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